CN113442860A - Charging management method and system for vehicle-mounted low-voltage storage battery - Google Patents

Charging management method and system for vehicle-mounted low-voltage storage battery Download PDF

Info

Publication number
CN113442860A
CN113442860A CN202110907775.7A CN202110907775A CN113442860A CN 113442860 A CN113442860 A CN 113442860A CN 202110907775 A CN202110907775 A CN 202110907775A CN 113442860 A CN113442860 A CN 113442860A
Authority
CN
China
Prior art keywords
sequence
current control
control period
vehicle
eng
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110907775.7A
Other languages
Chinese (zh)
Other versions
CN113442860B (en
Inventor
席利贺
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Jingwei Hirain Tech Co Ltd
Original Assignee
Beijing Jingwei Hirain Tech Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Jingwei Hirain Tech Co Ltd filed Critical Beijing Jingwei Hirain Tech Co Ltd
Priority to CN202110907775.7A priority Critical patent/CN113442860B/en
Publication of CN113442860A publication Critical patent/CN113442860A/en
Application granted granted Critical
Publication of CN113442860B publication Critical patent/CN113442860B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/03—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for supply of electrical power to vehicle subsystems or for
    • B60R16/033—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for supply of electrical power to vehicle subsystems or for characterised by the use of electrical cells or batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/03—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for supply of electrical power to vehicle subsystems or for
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Abstract

The invention discloses a charging management method and a charging management system for a vehicle-mounted low-voltage storage battery, when the voltage of a vehicle needs to be subjected to voltage transformation control, firstly, vehicle speed planning information of a current control period is obtained, and a target gear sequence of the current control period is calculated based on the vehicle speed planning information of the current control period; then, acquiring vehicle parameter information, and calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and a target gear sequence of the current control period; and finally, obtaining a target voltage optimization sequence of the current control period, and taking the target optimization voltage in the target voltage optimization sequence of the current control period as a target voltage decision value of the current control period. The invention fully utilizes the characteristic that the storage battery has the charging and discharging capacity with a certain depth, can carry out charging and discharging management on the storage battery in advance by combining traffic and road conditions, improves the energy use efficiency, avoids the over discharge of the electric quantity of the storage battery, and prolongs the service life of the storage battery.

Description

Charging management method and system for vehicle-mounted low-voltage storage battery
Technical Field
The invention relates to the technical field of automotive electronics, in particular to a charging management method and a charging management system for a vehicle-mounted low-voltage storage battery.
Background
In recent years, along with increasingly stringent emission regulations, energy conservation and environmental protection are more and more emphasized, and various automobile manufacturers concentrate on developing an oil saving technology of the whole automobile; in order to exert the fuel-saving potential of the vehicle to the maximum, the low-voltage system of the vehicle is more and more emphasized, the low-voltage system of the vehicle is mainly used for carrying out voltage transformation control on the target voltage of a generator or the voltage of other vehicle-mounted charging equipment, and comprehensively considering the state information of the vehicle, the state information of a storage battery and the like, and the aim of realizing the work of the electric quantity of the lead-acid storage battery in a non-full-power state is fulfilled.
The patent publication No. CN201811056485 discloses a control method and a control system of an intelligent generator, and the invention discloses that the charging mode of the intelligent generator to a lead-acid storage battery is divided into full charging, balanced charging and floating charging, wherein the balanced charging is in an optimal state. And determining the target voltage of the intelligent generator according to the engine performance mode and the target voltage corresponding table. The invention discloses a control mode of a target voltage of a variable generator, so that a lead-acid storage battery has certain chargeable space, and can absorb part of braking energy when a vehicle brakes, thereby achieving the purpose of energy conservation;
publication No. CN201410036262 discloses an automobile power management system and an automobile power management method, in the patent of the invention, a target regulation voltage value of a generator is calculated through a storage battery state acquisition module, a vehicle working condition information acquisition module and an engine management system, and a voltage regulation state of an intelligent generator is divided into: the system comprises a pre-voltage regulation state, a slow voltage regulation state, a full voltage regulation state, a normal voltage regulation state and a minimum voltage regulation state, and finally realizes the dynamic electric quantity balance among a generator, a storage battery and the load of the whole vehicle;
publication number CN201810336072 discloses a control method, device and management system for an intelligent generator, and the invention determines that the target voltage of the generator is switched between a first target voltage and a second target voltage according to the signal turn-on state of a headlamp, the running state of an engine and the deviation value of battery capacity. The invention reduces the voltage fluctuation of the headlight loop, improves the running stability of the headlight, increases the control flexibility of the intelligent generator and improves the experience of a driver.
According to the technical scheme, the charging method of the vehicle low-voltage storage battery in the prior art includes that the target voltage of the generator is determined through a preset target voltage meter by collecting the current state information of the vehicle, including the running state, the engine state, the gear position, the load state and the like of the vehicle, and finally the excitation current of the generator is controlled to realize that the voltage of the generator changes along with the target voltage. Although the traditional method can obtain a certain oil-saving control effect, the traditional storage battery charging method only makes a decision according to the current state of the vehicle due to lack of comprehensive consideration of road working condition information, the adaptability to actual road conditions and traffic conditions is poor, and the situation that the electric quantity of the storage battery deviates from the lower limit of a preset electric quantity window under complex road conditions occurs.
Therefore, how to avoid the deviation of the battery capacity from the lower limit of the preset capacity window under the complex road condition is an urgent problem to be solved.
Disclosure of Invention
In view of the above, the invention provides a charging management method and system for a vehicle-mounted low-voltage storage battery, which make full use of the characteristic that the storage battery has a certain depth of charging and discharging capability, can perform charging and discharging management on the storage battery in advance by combining traffic and road conditions, improve the energy utilization efficiency, avoid the over discharge of the electric quantity of the storage battery, and prolong the service life of the storage battery.
The invention provides a charging management method for a vehicle-mounted low-voltage storage battery, which comprises the following steps:
acquiring vehicle speed planning information of a current control period;
calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period;
acquiring vehicle parameter information;
calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period;
acquiring road condition information of a current control period;
calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period;
calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period;
acquiring storage battery planning information;
calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period;
calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period;
optimizing a target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period;
and taking the target optimized voltage in the target voltage optimized sequence of the current control period as a target voltage decision value of the current control period.
Preferably, the vehicle speed planning information includes a vehicle speed sequence { v [1], v [2], …, v [ K ] }, where K is a total number of time points in a prediction time window corresponding to the current control period, the target Gear sequence is { Gear [1], Gear [2], …, Gear [ K ] }, and a calculation method of Gear [ K ] corresponding to a kth time point in the target Gear sequence is as follows:
Gear[k]＝f(a(k)，v(k))
where K is 1,2, …, K, v (K) is a vehicle speed corresponding to the kth time point in the vehicle speed sequence, and a (K) is an acceleration corresponding to a vehicle speed v (K) corresponding to the kth time point in the vehicle speed sequence.
Preferably, the vehicle parameter information includes a vehicle driveline speed reduction factor itThe engine speed pre-sequencing column is { omega }Eng[1]，ωEng[2]，…，ωEng[K]The predicted engine speed omega corresponding to the kth time point in the engine speed prediction sequenceEng[k]The calculation method of (2) is as follows:
ωEng[k]＝Gear[k]*v(k)*it。
preferably, the vehicle parameter information further includes a total vehicle weight m, and a vehicle driveline efficiency ηtThe frontal area of the vehicle A; the road condition information of the current control period comprises a predicted slope angle sequence { a [1]]，a[2]，…，a[K]}; the vehicle required torque sequenceIs listed as { F [1]]，F[2]，…，F[K]-a vehicle demand torque F [ k ] corresponding to a kth time point in said vehicle demand torque sequence]The calculation method of (2) is as follows:
Figure BDA0003202364230000041
where δ is a rotating mass conversion coefficient, f is a rolling resistance coefficient, CDIs the coefficient of air resistance, g is the acceleration of gravity, ak]Is a ramp angle.
Preferably, the engine torque pre-sequencing column is { T }eng[1]，Teng[2]，…，Teng[K]An engine predicted torque T corresponding to a kth time point in the engine torque prediction sequenceeng[k]The calculation method of (2) is as follows:
Teng[k]＝F[k]*Gear[k]/it。
preferably, the battery planning information comprises a target voltage sequence { u } for the current control cyclei[1]，ui[2]，…，ui[K]And the current control period of the accumulator current sequence ibatt[1]，ibatt[2]，…，ibatt[K]}; the generator torque sequence is { T }gen[1]，Tgen[2]，…，Tgen[K]And the generator torque sequence T corresponding to the kth time point in the generator torque sequencegen[k]The calculation method of (2) is as follows:
Tgen[k]＝ui[k]*ibatt[k]/ωgen[k]
in the formula, ωgen[k]The kth generator speed in the generator speed sequence.
Preferably, the engine correction torque sequence is { T }eng_adj[1]，Teng_adj[2]，…，Teng_adj[K]R, engine correction torque T corresponding to k time point in the engine correction torque sequenceeng_adj[k]The calculation method of (2) is as follows:
Teng_adj[k]＝Teng[k]+Tgen[k]
a charge management system for an on-board low-voltage battery, comprising:
the first acquisition module is used for acquiring the vehicle speed planning information of the current control period;
the first calculation module is used for calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period;
the second acquisition module is used for acquiring vehicle parameter information;
the second calculation module is used for calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period;
the third acquisition module is used for acquiring the road condition information of the current control period;
the third calculation module is used for calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period;
the fourth calculation module is used for calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period;
the fourth acquisition module is used for acquiring the storage battery planning information;
the fifth calculation module is used for calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period;
the sixth calculation module is used for calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period;
the optimization module is used for optimizing a target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period;
and the execution module is used for taking the target optimized voltage in the target voltage optimized sequence of the current control period as the target voltage decision value of the current control period.
Preferably, the vehicle speed planning information includes a vehicle speed sequence { v [1], v [2], …, v [ K ] }, where K is a total number of time points in a prediction time window corresponding to the current control period, the target Gear sequence is { Gear [1], Gear [2], …, Gear [ K ] }, and a calculation method of Gear [ K ] corresponding to a kth time point in the target Gear sequence is as follows:
Gear[k]＝f(a(k)，v(k))
where K is 1,2, …, K, v (K) is a vehicle speed corresponding to the kth time point in the vehicle speed sequence, and a (K) is an acceleration corresponding to a vehicle speed v (K) corresponding to the kth time point in the vehicle speed sequence.
Preferably, the vehicle parameter information includes a vehicle driveline speed reduction factor itThe engine speed pre-sequencing column is { omega }Eng[1]，ωEng[2]，…，ωEng[K]The predicted engine speed omega corresponding to the kth time point in the engine speed prediction sequenceEng[k]The calculation method of (2) is as follows:
ωEng[k]＝Gear[k]*v(k)*it。
preferably, the vehicle parameter information further includes a total vehicle weight m, and a vehicle driveline efficiency ηtThe frontal area of the vehicle A; the road condition information of the current control period comprises a predicted slope angle sequence { a [1]]，a[2]，…，a[K]}; the vehicle required torque sequence is { F [1]]，F[2]，…，F[K]-a vehicle demand torque F [ k ] corresponding to a kth time point in said vehicle demand torque sequence]The calculation method of (2) is as follows:
Figure BDA0003202364230000061
where δ is a rotating mass conversion coefficient, f is a rolling resistance coefficient, CDIs the coefficient of air resistance, g is the acceleration of gravity, ak]Is a ramp angle.
Preferably, the engine torque pre-sequencing column is { T }eng[1]，Teng[2]，…，Teng[K]}，The predicted engine torque T corresponding to the kth time point in the predicted engine torque sequenceeng[k]The calculation method of (2) is as follows:
Teng[k]＝F[k]*Gear[k]/it。
preferably, the battery planning information comprises a target voltage sequence { u } for the current control cyclei[1]，ui[2]，…，ui[K]And the current control period of the accumulator current sequence ibatt[1]，ibatt[2]，…，ibatt[K]}; the generator torque sequence is { T }gen[1]，Tgen[2]，…，Tgen[K]And the generator torque sequence T corresponding to the kth time point in the generator torque sequencegen[k]The calculation method of (2) is as follows:
Tgen[k]＝ui[k]*ibatt[k]/ωgen[k]
in the formula, ωgen[k]The kth generator speed in the generator speed sequence.
Preferably, the engine correction torque sequence is { T }eng_adj[1]，Teng_adj[2]，…，Teng_adj[K]R, engine correction torque T corresponding to k time point in the engine correction torque sequenceeng_adj[k]The calculation method of (2) is as follows:
Teng_adj[k]＝Teng[k]+Tgen[k]。
in summary, the present invention discloses a charging management method and system for a vehicle-mounted low-voltage battery, when voltage transformation control of a vehicle is required, first, vehicle speed planning information of a current control period is obtained; calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period; then, vehicle parameter information is obtained; calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period; then, acquiring road condition information of the current control period; calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period; calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period; then, acquiring storage battery planning information; calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period; calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period; optimizing the target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period; and taking the target optimized voltage in the target voltage optimized sequence of the current control period as a target voltage decision value of the current control period. The invention fully utilizes the characteristic that the storage battery has the charging and discharging capacity with a certain depth, can carry out charging and discharging management on the storage battery in advance by combining traffic and road conditions, improves the energy use efficiency, avoids the over discharge of the electric quantity of the storage battery, and prolongs the service life of the storage battery.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method of embodiment 1 of a charging management method for an on-board low-voltage battery according to the present disclosure;
fig. 2 is a schematic structural diagram of embodiment 1 of a charging management system for an on-vehicle low-voltage battery according to the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, which is a flowchart of a method of embodiment 1 of a charging management method for an on-board low-voltage battery disclosed in the present invention, the method may include the following steps:
s101, acquiring vehicle speed planning information of a current control period;
in the process of automobile navigation or unmanned driving, a route can be planned based on a preset destination, and vehicle speed information can be automatically generated according to road conditions of the planned route. In the invention, one navigation can be regarded as one control cycle, or one navigation is divided into a plurality of control cycles, in addition, a prediction time window is arranged, the prediction time window comprises a plurality of time points with certain time intervals, each time point corresponds to one control cycle, for example, the current cycle speed is v 1, a dynamic programming algorithm is adopted to program the following cycle to be x 2 … … x K until x K, wherein the total number of the time points is K, and one time point is set as K. The vehicle speed planning information may include a vehicle speed sequence { v 1, v 2, …, vK }, where K is a total number of time points in a prediction time window corresponding to the current control period.
S102, calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period;
specifically, the target Gear sequence is { Gear [1], Gear [2], …, Gear [ K }, and Gear [ K ] corresponding to the kth time point in the target Gear sequence can be calculated as follows:
Gear[k]＝f(a(k),v(k))
in the formula, K is 1,2, …, K, v (K) is a vehicle speed corresponding to the kth time point in the vehicle speed sequence, and a (K) is an acceleration corresponding to the vehicle speed v (K) corresponding to the kth time point in the vehicle speed sequence.
And f, adopting a two-parameter gear shifting MAP, storing the corresponding relation among the vehicle speed, the acceleration and the target gear in the MAP, and inquiring the corresponding relation to obtain the target gear.
S103, acquiring vehicle parameter information;
the vehicle parameter information can be collected by sensors installed everywhere, can also be manually input by a driver, and can also be preset before the automobile leaves a factory. The vehicle parameter information may include a coefficient i of the vehicle driveline scaled rotational speedtTotal vehicle weight m, vehicle transmission system efficiency ηtAnd the frontal area A of the vehicle.
S104, calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period;
specifically, the engine speed pre-sequencing column is { omega }Eng[1],ωEng[2],…,ωEng[K]And (4) predicting the engine speed omega corresponding to the kth time point in the engine speed prediction sequenceEng[k]It can be calculated as follows:
ωEng[k]＝Gear[k]*v(k)*it。
s105, acquiring road condition information of the current control period;
in the process of automobile navigation or unmanned driving, a route can be planned based on a preset destination, and road condition information of the planned route can be acquired through equipment such as a GPS (global positioning system) and a Beidou navigation system. The traffic information of the current control period may include a sequence of predicted ramp angles { a [1], a [2], …, a [ K }.
S106, calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period;
specifically, the vehicle demand torque sequence is { F1, F2, …, FK }, and the vehicle demand torque Fk corresponding to the kth time point in the vehicle demand torque sequence can be calculated as follows:
Figure BDA0003202364230000101
wherein δ is the rotary massCoefficient of calculation, f is coefficient of rolling resistance, CDIs the coefficient of air resistance, g is the acceleration of gravity, ak]Is a ramp angle.
S107, calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period;
specifically, the engine torque pre-sequencing column is { T }eng[1]，Teng[2]，…，Teng[K]T, predicted engine torque T corresponding to kth time point in predicted engine torque sequenceeng[k]It can be calculated as follows:
Teng[k]＝F[k]*Gear[k]/it。
s108, obtaining storage battery planning information;
the battery planning information refers to the planning of the battery in the prior art, and may include the target voltage sequence { u } of the current control cyclei[1]，ui[2]，…，ui[K]And the current control period of the accumulator current sequence ibatt[1]，ibatt[2]，…，ibatt[K]}
S109, calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period;
specifically, the generator torque sequence is { T }gen[1]，Tgen[2]，…，Tgen[K]And h, the generator torque sequence T corresponding to the kth time point in the generator torque sequencegen[k]The calculation method of (2) is as follows:
Tgen[k]＝ui[k]*ibatt[k]/ωgen[k]
in the formula, ωgen[k]The kth generator speed in the generator speed sequence. The generator speed generally has a fixed transmission ratio relationship with the engine speed, i.e. a generator speed sequence can be derived from the engine speed, as follows
ωgen[k]＝ωeng[k]*γ
In the formula, gamma is the transmission ratio between the generator and the engine.
S110, calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period;
specifically, the engine correction torque sequence is { T }eng_adj[1]，Teng_adj[2]，…，Teng_adj[K]And f, engine correction torque T corresponding to the kth time point in the engine correction torque sequenceeng_adj[k]It can be calculated as follows:
Teng_adj[k]＝Teng[k]+Tgen[k]。
s111, optimizing a target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period;
specifically, the target voltage optimization sequence of the current control period can be obtained according to the following method:
(1) calculating the oil consumption sequence { m) of the current control periodfuel[1]，mfuel[2]，…，mfuel[K]And f, oil consumption m corresponding to the kth time point in the oil consumption sequence of the current control periodfuel[k]The sequence omega can be predicted according to the engine speed of the current control cycleEng[k]And the engine correction torque sequence T of the current control periodeng_adj[k]And queries the engine fuel consumption MAP.
(2) And solving the following state equation, objective function and boundary condition by using a dynamic programming global optimization algorithm to obtain an objective voltage optimization sequence.
The state equation is as follows:
SoC[k+1]＝SoC[k]-ibatt(k)*Δt*η/Cbatt
wherein, Δ t is the interval time of two adjacent time points in the current control cycle, SoC [ k + 1]]And SoC [ k ]]Respectively corresponding to the (k + 1) th time point of the current control cycle and the kth time point of the current control cyclebatt(k) Is the k time of the current control cycleThe charging and discharging current values of the storage battery corresponding to the intermediate points, eta is the charging and discharging efficiency under the front SOC, is a storage battery characteristic parameter and can be obtained by looking up a table, CbattIs the battery capacity;
ibatt(k)＝(ui(k)-Uocv/Rint)
in the formula, Uocv is the open-circuit voltage of the storage battery, is a storage battery characteristic parameter, and can be obtained by looking up a table, and Rint is the internal resistance of the storage battery, is a storage battery characteristic parameter, and can be obtained by looking up a table.
An objective function:
J＝min(α*mfuel[k]+β*(SoC[k]-SoCtrgt))
in the formula, J is a target cost function, alpha is a weight factor of a predicted fuel consumption sequence, beta is a weight factor of the change of the electric quantity of the storage battery, and SoCtrgtIs a battery charge target value, mfuel[k]The fuel consumption corresponding to the kth time point in the fuel consumption sequence of the current control period.
The value of the target voltage of the generator can influence the oil consumption of the engine and the overall working efficiency of the system, and the optimal target voltage control sequence of the generator is obtained in a prediction time window, and the control sequence needs to ensure that the state of charge value of the storage battery cannot deviate from the target value of the charge state of the storage battery SoC too muchtrgt。
Constraint conditions are as follows:
SoCmin≤SoC(k)≤SoCmax
Tmin≤Teng(k)≤Tmax
umin≤Δu(k)≤umax
in the formula, SoCminIs the lowest SOC limit value of the accumulatormaxMaximum limit for the chargeable capacity of the accumulator, TminFor the minimum torque, T, output during engine operationmaxIs the maximum torque, u, output when the engine is runningminIs the minimum value of the generator voltage variation, umaxAnd the maximum value of the voltage change of the generator is delta u (k), and the target voltage change value corresponding to the kth time point of the current control period is delta u (k).
S112, taking the target optimized voltage in the target voltage optimized sequence of the current control period as a target voltage decision value of the current control period;
the target voltage optimization sequence is { u 1, u 2, …, u K }, and after the target voltage optimization sequence is obtained, the target optimization voltage in the target voltage optimization sequence is sequentially used as a target voltage decision value to control the storage battery according to the sequence.
In summary, in the above embodiment, when the voltage of the vehicle needs to be controlled through voltage transformation, first, the vehicle speed planning information of the current control period is obtained; calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period; then, vehicle parameter information is obtained; calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period; then, acquiring road condition information of the current control period; calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period; calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period; then, acquiring storage battery planning information; calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period; calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period; optimizing the target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period; and taking the target optimized voltage in the target voltage optimized sequence of the current control period as a target voltage decision value of the current control period. The invention fully utilizes the characteristic that the storage battery has the charging and discharging capacity with a certain depth, can carry out charging and discharging management on the storage battery in advance by combining traffic and road conditions, improves the energy use efficiency, avoids the over discharge of the electric quantity of the storage battery, and prolongs the service life of the storage battery.
As shown in fig. 2, which is a schematic structural diagram of embodiment 1 of a charging management system for an on-vehicle low-voltage battery disclosed in the present invention, the system may include:
the first obtaining module 201 is used for obtaining vehicle speed planning information of a current control period;
in the process of automobile navigation or unmanned driving, a route can be planned based on a preset destination, and vehicle speed information can be automatically generated according to road conditions of the planned route. In the present invention, one navigation may be regarded as one control period, or one navigation may be divided into a plurality of control periods, and each control period includes a plurality of time points spaced at a certain time. The vehicle speed planning information may include a vehicle speed sequence { v 1, v 2, …, vK }, where K is a total number of time points in a prediction time window corresponding to the current control period.
The first calculation module 202 is used for calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period;
specifically, the target Gear sequence is { Gear [1], Gear [2], …, Gear [ K }, and Gear [ K ] corresponding to the kth time point in the target Gear sequence can be calculated as follows:
Gear[k]＝f(a(k)，v(k))
in the formula, K is 1,2, …, K, v (K) is a vehicle speed corresponding to the kth time point in the vehicle speed sequence, and a (K) is an acceleration corresponding to the vehicle speed v (K) corresponding to the kth time point in the vehicle speed sequence.
The second acquisition module 203 is used for acquiring vehicle parameter information;
the vehicle parameter information can be collected by sensors installed everywhere, can also be manually input by a driver, and can also be preset before the automobile leaves a factory. The vehicle parameter information may include a coefficient i of the vehicle driveline scaled rotational speedtTotal vehicle weight m, vehicle transmission system efficiency ηtAnd the frontal area A of the vehicle.
The second calculation module 204 is used for calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period;
specifically, the engine speed pre-sequencing column is { omega }Eng[1]，ωEng[2]，…，ωEng[K]And (4) predicting the engine speed omega corresponding to the kth time point in the engine speed prediction sequenceEng[k]It can be calculated as follows:
ωEng[k]＝Gear[k]*v(k)*it。
a third obtaining module 205, obtaining the traffic information of the current control period;
in the process of automobile navigation or unmanned driving, a route can be planned based on a preset destination, and road condition information of the planned route can be acquired through a GPS (global positioning system), a Beidou navigation system and the like. The traffic information of the current control period may include a sequence of predicted ramp angles { a 1, a 2, …, a K ] },
a third calculating module 206, for calculating the vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period;
specifically, the vehicle demand torque sequence is { F1, F2, …, FK }, and the vehicle demand torque Fk corresponding to the kth time point in the vehicle demand torque sequence can be calculated as follows:
Figure BDA0003202364230000151
where δ is a rotating mass conversion coefficient, f is a rolling resistance coefficient, CDIs the coefficient of air resistance, g is the acceleration of gravity, ak]Is a ramp angle.
The fourth calculation module 207 is used for calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period;
specifically, the engine torque pre-sequencing column is { T }eng[1]，Teng[2]，…，Teng[K]The engine prediction corresponding to the kth time point in the engine torque prediction sequenceTorque Teng[k]It can be calculated as follows:
Teng[k]＝F[k]*Gear[k]/it。
a fourth obtaining module 208, which obtains the storage battery planning information;
the battery planning information refers to the planning of the battery in the prior art, and may include the target voltage sequence { u } of the current control cyclei[1]，ui[2]，…，ui[K]And the current control period of the accumulator current sequence ibatt[1]，ibatt[2]，…，ibatt[K]}。
A fifth calculation module 209, which calculates a generator torque sequence of the current control period based on the battery planning information and the engine speed prediction sequence of the current control period;
specifically, the generator torque sequence is { T }gen[1]，Tgen[2]，…，Tgen[K]And h, the generator torque sequence T corresponding to the kth time point in the generator torque sequencegen[k]The calculation method of (2) is as follows:
Tgen[k]＝ui[k]*ibatt[k]/ωgen[k]
in the formula, ωgen[k]The kth generator speed in the generator speed sequence.
A sixth calculation module 210 that calculates an engine correction torque sequence for the current control cycle based on the engine torque prediction sequence for the current control cycle and the generator torque sequence for the current control cycle;
specifically, the engine correction torque sequence is { T }eng_adj[1]，Teng_adj[2]，…，Teng_adj[K]And f, engine correction torque T corresponding to the kth time point in the engine correction torque sequenceeng_adj[k]It can be calculated as follows:
Teng_adj[k]＝Teng[k]+Tgen[k]。
the optimization module 211 is used for optimizing the target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period;
specifically, the target voltage optimization sequence of the current control period can be obtained according to the following method:
(1) calculating the oil consumption sequence { m) of the current control periodfuel[1]，mfuel[2]，…，mfuel[K]And f, oil consumption m corresponding to the kth time point in the oil consumption sequence of the current control periodfuel[k]The sequence omega can be predicted according to the engine speed of the current control cycleEng[k]And the engine correction torque sequence T of the current control periodeng_adj[k]And queries the engine fuel consumption MAP.
(2) And solving the following state equation, objective function and boundary condition by using a dynamic programming global optimization algorithm to obtain an objective voltage optimization sequence.
The state equation is as follows:
SoC[k+1]＝SoC[k]-ibatt(k)*Δt*η/Cbatt
wherein, Δ t is the interval time of two adjacent time points in the current control cycle, SoC [ k + 1]]And the SoC just respectively is the storage battery state of charge value corresponding to the kth +1 time point of the current control period and the storage battery state of charge value corresponding to the kth time point of the current control period, ibatt(k) The charging and discharging current value of the storage battery corresponding to the kth time point in the current control period is defined, eta is the charging and discharging efficiency under the previous SOC, is a storage battery characteristic parameter and can be obtained by table lookup, CbattIs the battery capacity;
ibatt(k)＝(ui(k)-Uocv/Rint)
in the formula, Uocv is the open-circuit voltage of the storage battery, is a storage battery characteristic parameter, and can be obtained by looking up a table, and Rint is the internal resistance of the storage battery, is a storage battery characteristic parameter, and can be obtained by looking up a table.
An objective function:
J＝min(α*mfuel[k]+β*(SoC[k]-SoCtrgt))
in the formula, J is a target cost function, alpha is a predicted fuel consumption sequence, beta is a weight factor of the change of the electric quantity of the storage battery, and SoCtrgtIs a battery charge target value, mfuel[k]The fuel consumption corresponding to the kth time point in the fuel consumption sequence of the current control period.
The value of the target voltage of the generator can influence the oil consumption of the engine and the overall working efficiency of the system, and the optimal target voltage control sequence of the generator is obtained in a prediction time window, and the control sequence needs to ensure that the state of charge value of the storage battery cannot deviate from the target value of the charge state of the storage battery SoC too muchtrgt。
Constraint conditions are as follows:
SoCmin≤SoC(k)≤SoCmax
Tmin≤Teng(k)≤Tmax
umin≤Δu(k)≤umax
in the formula, SoCminIs the lowest SOC limit value of the accumulatormaxMaximum limit for the chargeable capacity of the accumulator, TminFor the minimum torque, T, output during engine operationmaxIs the maximum torque, u, output when the engine is runningminIs the minimum value of the generator voltage variation, umaxAnd the maximum value of the voltage change of the generator is delta u (k), and the target voltage change value corresponding to the kth time point of the current control period is delta u (k).
The execution module 212 takes the target optimized voltage in the target voltage optimized sequence of the current control period as the target voltage decision value of the current control period;
the target voltage optimization sequence is { u 1, u 2, …, u K }, and after the target voltage optimization sequence is obtained, the target optimization voltage in the target voltage optimization sequence is sequentially used as a target voltage decision value to control the storage battery according to the sequence.
The operation principle of the charging management system for the vehicle-mounted low-voltage storage battery disclosed in this embodiment is the same as that of the charging management method for the vehicle-mounted low-voltage storage battery embodiment 1, and details are not repeated here.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A charge management method for an on-vehicle low-voltage battery, characterized by comprising:
acquiring vehicle speed planning information of a current control period;
calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period;
acquiring vehicle parameter information;
calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period;
acquiring road condition information of a current control period;
calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period;
calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period;
acquiring storage battery planning information;
calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period;
calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period;
optimizing a target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period;
and taking the target optimized voltage in the target voltage optimized sequence of the current control period as a target voltage decision value of the current control period.
2. The charge management method for the vehicle-mounted low-voltage storage battery according to claim 1, wherein the vehicle speed planning information comprises a vehicle speed sequence { v [1], v [2], …, v [ K ] }, where K is a total number of time points in a predicted time window corresponding to the current control cycle, the target Gear sequence is { Gear [1], Gear [2], …, Gear [ K ] }, and a calculation method of Gear [ K ] corresponding to a kth time point in the target Gear sequence is as follows:
Gear[k]＝f(a(k)，v(k))
where K is 1,2, …, K, v (K) is a vehicle speed corresponding to the kth time point in the vehicle speed sequence, and a (K) is an acceleration corresponding to a vehicle speed v (K) corresponding to the kth time point in the vehicle speed sequence.
3. The charge management method for an on-board low-voltage battery according to claim 2, wherein the vehicle parameter information includes a coefficient i of a vehicle driveline reduced rotation speedtThe engine speed pre-sequencing column is { omega }Eng[1]，ωEng[2]，…，ωEng[K]The predicted engine speed omega corresponding to the kth time point in the engine speed prediction sequenceEng[k]The calculation method of (2) is as follows:
ωEng[k]＝Gear[k]*v(k)*it。
4. the charge management method for an on-board low-voltage battery according to claim 3, wherein the vehicle parameter information further includes a vehicle total weight m, a vehicle transmission system efficiency ηtThe frontal area of the vehicle A; the road condition information of the current control period comprises a predicted slope angle sequence { a [1]]，a[2]，…，a[K]}; the vehicle required torque sequence is { F [1]]，F[2]，…，F[K]-a vehicle demand torque F [ k ] corresponding to a kth time point in said vehicle demand torque sequence]The calculation method of (2) is as follows:
Figure FDA0003202364220000021
where δ is a rotating mass conversion coefficient, f is a rolling resistance coefficient, CDIs the coefficient of air resistance, g is the acceleration of gravity, ak]Is a ramp angle.
5. The charge management method for an on-vehicle low-voltage battery according to claim 4, characterized in that said methodThe motive torque pre-sequencing column is { T }eng[1]，Teng[2]，…，Teng[K]An engine predicted torque T corresponding to a kth time point in the engine torque prediction sequenceeng[k]The calculation method of (2) is as follows:
Teng[k]＝F[k]*Gear[k]/it。
6. the charge management method for the on-vehicle low-voltage battery according to claim 5, wherein the battery-plan information includes a target voltage sequence { u } of the current control cyclei[1]，ui[2]，…，ui[K]And the current control period of the accumulator current sequence ibatt[1]，ibatt[2]，…，ibatt[K]}; the generator torque sequence is { T }gen[1]，Tgen[2]，…，Tgen[K]And the generator torque sequence T corresponding to the kth time point in the generator torque sequencegen[k]The calculation method of (2) is as follows:
Tgen[k]＝ui[k]*ibatt[k]/ωgen[k]
in the formula, ωgen[k]The kth generator speed in the generator speed sequence.
7. The charge management method for the on-vehicle low-voltage battery according to claim 6, wherein the engine correction torque sequence is { T } Teng_adj[1]，Teng_adj[2]，…，Teng_adj[K]R, engine correction torque T corresponding to k time point in the engine correction torque sequenceeng_adj[k]The calculation method of (2) is as follows:
Teng_adj[k]＝Teng[k]+Tgen[k]。
8. a charge management system for an on-board low-voltage battery, comprising:
the first acquisition module is used for acquiring the vehicle speed planning information of the current control period;
the first calculation module is used for calculating a target gear sequence of the current control period based on the vehicle speed planning information of the current control period;
the second acquisition module is used for acquiring vehicle parameter information;
the second calculation module is used for calculating an engine speed prediction sequence of the current control period based on the vehicle parameter information and the target gear sequence of the current control period;
the third acquisition module is used for acquiring the road condition information of the current control period;
the third calculation module is used for calculating a vehicle demand torque sequence of the current control period based on the vehicle parameter information, the road condition information of the current control period and the vehicle speed planning information of the current control period;
the fourth calculation module is used for calculating an engine torque prediction sequence of the current control period based on the vehicle demand torque sequence of the current control period and the target gear sequence of the current control period;
the fourth acquisition module is used for acquiring the storage battery planning information;
the fifth calculation module is used for calculating a generator torque sequence of the current control period based on the storage battery planning information and the engine speed prediction sequence of the current control period;
the sixth calculation module is used for calculating an engine correction torque sequence of the current control period based on the engine torque prediction sequence of the current control period and the generator torque sequence of the current control period;
the optimization module is used for optimizing a target voltage sequence based on the engine speed prediction sequence of the current control period and the engine correction torque sequence of the current control period to obtain a target voltage optimization sequence of the current control period;
and the execution module is used for taking the target optimized voltage in the target voltage optimized sequence of the current control period as the target voltage decision value of the current control period.
9. The charge management system for the on-vehicle low-voltage battery according to claim 8, wherein the vehicle speed schedule information includes a vehicle speed sequence { v [1], v [2], …, v [ K ] }, where K is a total number of time points in a predicted time window corresponding to the current control cycle, the target Gear sequence is { Gear [1], Gear [2], …, Gear [ K ] }, and a calculation method of Gear [ K ] corresponding to a kth time point in the target Gear sequence is as follows:
Gear[k]＝f(a(k)，v(k))
where K is 1,2, …, K, v (K) is a vehicle speed corresponding to the kth time point in the vehicle speed sequence, and a (K) is an acceleration corresponding to a vehicle speed v (K) corresponding to the kth time point in the vehicle speed sequence.
10. The charge management system for an on-board low-voltage battery according to claim 9, wherein said vehicle parameter information includes a vehicle driveline reduced speed coefficient itThe engine speed pre-sequencing column is { omega }Eng[1]，ωEng[2]，…，ωEng[K]The predicted engine speed omega corresponding to the kth time point in the engine speed prediction sequenceEng[k]The calculation method of (2) is as follows:
ωEng[k]＝Gear[k]*v(k)*it。
CN202110907775.7A 2021-08-09 2021-08-09 Charging management method and system for vehicle-mounted low-voltage storage battery Active CN113442860B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110907775.7A CN113442860B (en) 2021-08-09 2021-08-09 Charging management method and system for vehicle-mounted low-voltage storage battery

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110907775.7A CN113442860B (en) 2021-08-09 2021-08-09 Charging management method and system for vehicle-mounted low-voltage storage battery

Publications (2)

Publication Number Publication Date
CN113442860A true CN113442860A (en) 2021-09-28
CN113442860B CN113442860B (en) 2022-12-20

Family

ID=77818293

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110907775.7A Active CN113442860B (en) 2021-08-09 2021-08-09 Charging management method and system for vehicle-mounted low-voltage storage battery

Country Status (1)

Country Link
CN (1) CN113442860B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103600742A (en) * 2013-12-03 2014-02-26 北京交通大学 Energy management control device of hybrid electric vehicle and method for energy management control
CN106064614A (en) * 2015-04-17 2016-11-02 三菱电机株式会社 Vehicle energy management apparatus
CN106803692A (en) * 2015-11-26 2017-06-06 广州汽车集团股份有限公司 Automobile current generator intelligent charge control method and control system
CN109421689A (en) * 2017-09-04 2019-03-05 郑州宇通客车股份有限公司 A kind of automobile energy-saving control method, system and vehicle
CN111409576A (en) * 2019-01-08 2020-07-14 郑州宇通客车股份有限公司 Vehicle generator control method and system
CN111963308A (en) * 2020-07-13 2020-11-20 宝能(西安)汽车研究院有限公司 Battery control method for vehicle and vehicle

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103600742A (en) * 2013-12-03 2014-02-26 北京交通大学 Energy management control device of hybrid electric vehicle and method for energy management control
CN106064614A (en) * 2015-04-17 2016-11-02 三菱电机株式会社 Vehicle energy management apparatus
CN106803692A (en) * 2015-11-26 2017-06-06 广州汽车集团股份有限公司 Automobile current generator intelligent charge control method and control system
CN109421689A (en) * 2017-09-04 2019-03-05 郑州宇通客车股份有限公司 A kind of automobile energy-saving control method, system and vehicle
CN111409576A (en) * 2019-01-08 2020-07-14 郑州宇通客车股份有限公司 Vehicle generator control method and system
CN111963308A (en) * 2020-07-13 2020-11-20 宝能(西安)汽车研究院有限公司 Battery control method for vehicle and vehicle

Also Published As

Publication number Publication date
CN113442860B (en) 2022-12-20

Similar Documents

Publication Publication Date Title
US7849944B2 (en) Self-learning control system for plug-in hybrid vehicles
EP2931551B1 (en) Range extender control
US8548660B2 (en) Integrated hybrid vehicle control strategy
JP5736115B2 (en) Vehicles that optimize the use of energy storage components
US8374740B2 (en) Self-learning satellite navigation assisted hybrid vehicle controls system
US9676382B2 (en) Systems and methods for hybrid vehicles with a high degree of hybridization
CN111959490B (en) Reference self-adaptive optimal energy management method for plug-in hybrid electric vehicle model
JP4637443B2 (en) Vehicle control system and control method
US8428804B2 (en) In-vehicle charge and discharge control apparatus and partial control apparatus
CN109263640A (en) Route energy predicting, amendment and the vehicle of optimization based on model
CN1476396A (en) Hybrid power sources distribution management
CN101384460A (en) Hybrid vehicle controller
CN101813754A (en) State estimating method for automobile start illumination type lead-acid storage battery
CN110356397B (en) Hybrid electric vehicle optimization method based on energy normalization minimization of road gradient
JP2020505263A (en) Methods for optimizing the energy consumption of hybrid vehicles
CN113071474B (en) Energy management method and system of vehicle and vehicle
JP2019171916A (en) Vehicle with power generator
CN116946107B (en) Hybrid system mode decision and power distribution method under energy track following
CN114148325B (en) Method for managing predictive energy of heavy-duty hybrid commercial vehicle
GB2561409A (en) Methods and systems for managing range of a vehicle
CN113442860B (en) Charging management method and system for vehicle-mounted low-voltage storage battery
CN113147321A (en) Vehicle-mounted air conditioner and regenerative braking coordination control method
JP2018114815A (en) Vehicle control device
CN116674399A (en) Range extender control method and device in running state and vehicle
Liu et al. A robust design method for optimal engine operating zone design of plug-in hybrid electric bus

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant